Bibliographic record
Abstract
PURPOSE OF REVIEW: This study outlines the rationale and provides evidence in support of including psychiatric disorders in the World Health Organization's classification of preventable diseases. The methods used represent a novel approach to describe clinical pathways, highlighting the importance of considering the full range of comorbid disorders within an integrated population-based data repository. RECENT FINDINGS: Review of literature focused on comorbidity in relation to the four preventable diseases identified by the World Health Organization. This revealed that only 29 publications over the last 5 years focus on populations and tend only to consider one or two comorbid disorders simultaneously in regard to any main preventable disease class. SUMMARY: This article draws attention to the importance of physical and psychiatric comorbidity and illustrates the complexity related to describing clinical pathways in terms of understanding the etiological and prognostic clinical profile for patients. Developing a consistent and standardized approach to describe these features of disease has the potential to dramatically shift the format of both clinical practice and medical education when taking into account the complex relationships between and among diseases, such as psychiatric and physical disease, that, hitherto, have been largely unrelated in research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".